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https://github.com/aimclub/BAMT
Repository of a data modeling and analysis tool based on Bayesian networks
https://github.com/aimclub/BAMT
bayesian-networks mixed-data parameters-learning structure-learning synthetic-data
Last synced: 2 months ago
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Repository of a data modeling and analysis tool based on Bayesian networks
- Host: GitHub
- URL: https://github.com/aimclub/BAMT
- Owner: aimclub
- License: bsd-3-clause
- Created: 2020-06-08T16:52:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-14T16:39:29.000Z (3 months ago)
- Last Synced: 2024-11-08T04:03:21.280Z (3 months ago)
- Topics: bayesian-networks, mixed-data, parameters-learning, structure-learning, synthetic-data
- Language: Python
- Homepage: https://bamt.readthedocs.io
- Size: 106 MB
- Stars: 120
- Watchers: 10
- Forks: 17
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- Security: SECURITY.md
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README
# BAMT - Bayesian Analytical and Modelling Toolkit
Repository of a data modeling and analysis tool based on Bayesian networks.
## Badges
| team |   |
|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| package |    |
| tests |   |
| docs |  |
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## Introduction
BAMT - Bayesian Analytical and Modelling Toolkit. This repository contains a data modeling and analysis tool based on Bayesian networks. It can be divided into two main parts - algorithms for constructing and training Bayesian networks on data and algorithms for applying Bayesian networks for filling gaps, generating synthetic data, assessing edge strength, etc.
## Installation
BAMT package is available via PyPi:
```bash
pip install bamt
```
## BAMT Features
The following algorithms for Bayesian Networks learning are implemented:
- Building the structure of a Bayesian network based on expert knowledge by directly specifying the structure of the network.
- Building the structure of a Bayesian network on data using three algorithms - Hill Climbing, evolutionary, and PC (PC is currently under development). For Hill Climbing, the following score functions are implemented - MI, K2, BIC, AIC. The algorithms work on both discrete and mixed data.
- Learning the parameters of distributions in the nodes of the network based on Gaussian distribution and Mixture Gaussian distribution with automatic selection of the number of components.
- Non-parametric learning of distributions at nodes using classification and regression models.
- BigBraveBN - algorithm for structural learning of Bayesian networks with a large number of nodes. Tested on networks with up to 500 nodes.
### Difference from existing implementations:
- Algorithms work on mixed data.
- Structural learning implements score-functions for mixed data.
- Parametric learning implements the use of a mixture of Gaussian distributions to approximate continuous distributions.
- Non-parametric learning of distributions with various user-specified regression and classification models.
- The algorithm for structural training of large Bayesian networks (> 10 nodes) is based on local training of small networks with their subsequent algorithmic connection.
For example, in terms of data analysis and modeling using Bayesian networks, a pipeline has been implemented to generate synthetic data by sampling from Bayesian networks.
## How to use
Then the necessary classes are imported from the library:
```python
from bamt.networks.hybrid_bn import HybridBN
```
Next, a network instance is created and training (structure and parameters) is performed:
```python
bn = HybridBN(has_logit=False, use_mixture=True)
bn.add_edges(preprocessed_data)
bn.fit_parameters(data)
```
## Examples & Tutorials
More examples can be found in [Documentation](https://bamt.readthedocs.io/en/latest/examples/learn_save.html).
## Publications about BAMT
We have published several articles about BAMT:
- [Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models](https://www.mdpi.com/2227-7390/11/2/343) (2023)
- [BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes](https://www.sciencedirect.com/science/article/pii/S1877050922016945) (2022)
- [MIxBN: Library for learning Bayesian networks from mixed data](https://www.sciencedirect.com/science/article/pii/S1877050921020925) (2021)
- [Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks](https://link.springer.com/chapter/10.1007/978-3-030-77961-0_33) (2021)
- [Bayesian Networks-based personal data synthesis](https://dl.acm.org/doi/abs/10.1145/3411170.3411243) (2020)
## Project structure
The latest stable version of the library is available in the master branch.
It includes the following modules and directories:
- [bamt](https://github.com/ITMO-NSS-team/BAMT/tree/master/bamt) - directory with the framework code:
- Preprocessing - module for data preprocessing
- Networks - module for building and training Bayesian networks
- Nodes - module for nodes support of Bayesian networks
- Utilities - module for mathematical and graph utilities
- [data](https://github.com/ITMO-NSS-team/BAMT/tree/master/data) - directory with data for experiments and tests
- [tests](https://github.com/ITMO-NSS-team/BAMT/tree/master/tests) - directory with unit and integration tests
- [tutorials](https://github.com/ITMO-NSS-team/BAMT/tree/master/tutorials) - directory with tutorials
- [docs](https://github.com/ITMO-NSS-team/BAMT/tree/master/docs) - directory with RTD documentation
### Preprocessing
Preprocessor module allows users to transform data according to the pipeline (similar to the pipeline in scikit-learn).
### Networks
Three types of networks are implemented:
- HybridBN - Bayesian network with mixed data
- DiscreteBN - Bayesian network with discrete data
- ContinuousBN - Bayesian network with continuous data
They are inherited from the abstract class BaseNetwork.
### Nodes
Contains classes for nodes of Bayesian networks.
### Utilities
Utilities module contains mathematical and graph utilities to support the main functionality of the library.
## Web-BAMT
A web interface for BAMT is currently under development. The repository is available at [web-BAMT](https://github.com/aimclub/Web-BAMT).
## Contacts
If you have questions or suggestions, you can contact us at the following address: [email protected] (Irina Deeva)
Our resources:
- [Natural Systems Simulation Team](https://itmo-nss-team.github.io/)
- [NSS team Telegram channel](https://t.me/NSS_group)
- [NSS lab YouTube channel](https://www.youtube.com/@nsslab/videos)
## Citation
```bibtex
@misc{BAMT,
author={BAMT},
title = {Repository experiments and data},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ITMO-NSS-team/BAMT.git}},
url = {https://github.com/ITMO-NSS-team/BAMT.git}
}
@article{deeva2023advanced,
title={Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models},
author={Deeva, Irina and Bubnova, Anna and Kalyuzhnaya, Anna V},
journal={Mathematics},
volume={11},
number={2},
pages={343},
year={2023},
}
```